Samuele Salti | Università di Bologna (original) (raw)

Papers by Samuele Salti

Abstract Motivated by the increasing availability of 3D sensors capable of delivering both shape ... more Abstract Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh.

Abstract In the past few years detection of repeatable and distinctive keypoints on 3D surfaces h... more Abstract In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors.

… of the ACM workshop on 3D …, Jan 1, 2010

Computer Vision–ACCV 2010, Jan 1, 2011

Computer Vision–ECCV 2010, Jan 1, 2010

Collection of distance measurements from the sensor to the surface Distances are them transformed... more Collection of distance measurements from the sensor to the surface Distances are them transformed into 3D coordinates (x, y, z) by means of calibration information Usually, 3D sensors (or 3D scanners) acquire only a view of the object (2.5 D data) Some sensors also acquire information concerning color or light intensity (RGB-D data) First step of every 3D reconstruction/3D recognition pipeline

We present a background subtraction approach aimed at efficiency and robustness to common source ... more We present a background subtraction approach aimed at efficiency and robustness to common source of disturbance such as gradual and sudden illumination changes, camera gain and exposure variations, noise. At each new frame, a non-parametric mixture-based probabilistic clustering is performed to segment the image into changed and unchanged pixels with respect to a fixed background. A two-components mixture, a two-dimensional discrete feature space, a non-parametric model for the components likelihood and a proper initial guess are the key ingredients of this novel algorithm that, besides dealing effectively with the discrimination of photometric and semantic changes, exhibits very high computational efficiency. Experiments are presented, proving the achieved state-of-the-art robustness-efficiency trade-off.

Motivated by the increasing availability of 3D sensors capable of delivering both shape and textu... more Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions.

Intense research activity on 3D data analysis tasks, such as object recognition and shape retriev... more Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive keypoints in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Motivated by this, our work proposes a performance evaluation of the state-of-the-art in 3D keypoint detection, mainly addressing the task of 3D object recognition. The evaluation is carried out by analyzing the performance of several prominent methods in terms of robustness to noise (real and synthetic), presence of clutter, occlusions and point-of-view variations.

The ability of recognizing object categories in 3D data is still an underdeveloped topic. This pa... more The ability of recognizing object categories in 3D data is still an underdeveloped topic. This paper investigates on adopting Implicit Shape Models (ISMs) for 3D categorization, that, differently from current approaches, include also information on the geometrical structure of each object category. ISMs have been originally proposed for recognition and localization of categories in cluttered images. Modifications to allow for a correct deployment for 3D data are discussed. Moreover, we propose modifications to three design points within the structure of a standard ISM to enhance its effectiveness for the categorization of databases entries, either 3D or 2D: namely, codebook size and composition, codeword activation strategy and vote weight strategy. Experimental results on two standard 3D datasets allow us to discuss the positive impact of the proposed modifications as well as to show the performance in recognition accuracy yielded by our approach compared to the state of the art.

Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for a... more Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for applications in other domains requiring hidden state estimation. Although theoretically sound and unquestionably powerful, from a practical point of view RBE suffers from the assumption of complete a priori knowledge of the transition model, that is typically unknown. The use of wrong a priori transition model may lead to large estimation errors or even to divergence.
This work proposes to prevent these problems, in case of fully observable systems, learning the transition model on-line via Support Vector Regression. An application of this general framework is proposed in the context of linear/Gaussian systems and shown to be superior to a standard, non adaptive solution.

This paper deals with local 3D descriptors for surface matching. First, we categorize existing me... more This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms.
Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and
robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.

Abstract Motivated by the increasing availability of 3D sensors capable of delivering both shape ... more Abstract Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh.

Abstract In the past few years detection of repeatable and distinctive keypoints on 3D surfaces h... more Abstract In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors.

… of the ACM workshop on 3D …, Jan 1, 2010

Computer Vision–ACCV 2010, Jan 1, 2011

Computer Vision–ECCV 2010, Jan 1, 2010

Collection of distance measurements from the sensor to the surface Distances are them transformed... more Collection of distance measurements from the sensor to the surface Distances are them transformed into 3D coordinates (x, y, z) by means of calibration information Usually, 3D sensors (or 3D scanners) acquire only a view of the object (2.5 D data) Some sensors also acquire information concerning color or light intensity (RGB-D data) First step of every 3D reconstruction/3D recognition pipeline

We present a background subtraction approach aimed at efficiency and robustness to common source ... more We present a background subtraction approach aimed at efficiency and robustness to common source of disturbance such as gradual and sudden illumination changes, camera gain and exposure variations, noise. At each new frame, a non-parametric mixture-based probabilistic clustering is performed to segment the image into changed and unchanged pixels with respect to a fixed background. A two-components mixture, a two-dimensional discrete feature space, a non-parametric model for the components likelihood and a proper initial guess are the key ingredients of this novel algorithm that, besides dealing effectively with the discrimination of photometric and semantic changes, exhibits very high computational efficiency. Experiments are presented, proving the achieved state-of-the-art robustness-efficiency trade-off.

Motivated by the increasing availability of 3D sensors capable of delivering both shape and textu... more Motivated by the increasing availability of 3D sensors capable of delivering both shape and texture information, this paper presents a novel descriptor for feature matching in 3D data enriched with texture. The proposed approach stems from the theory of a recently proposed descriptor for 3D data which relies on shape only, and represents its generalization to the case of multiple cues associated with a 3D mesh. The proposed descriptor, dubbed CSHOT, is demonstrated to notably improve the accuracy of feature matching in challenging object recognition scenarios characterized by the presence of clutter and occlusions.

Intense research activity on 3D data analysis tasks, such as object recognition and shape retriev... more Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive keypoints in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Motivated by this, our work proposes a performance evaluation of the state-of-the-art in 3D keypoint detection, mainly addressing the task of 3D object recognition. The evaluation is carried out by analyzing the performance of several prominent methods in terms of robustness to noise (real and synthetic), presence of clutter, occlusions and point-of-view variations.

The ability of recognizing object categories in 3D data is still an underdeveloped topic. This pa... more The ability of recognizing object categories in 3D data is still an underdeveloped topic. This paper investigates on adopting Implicit Shape Models (ISMs) for 3D categorization, that, differently from current approaches, include also information on the geometrical structure of each object category. ISMs have been originally proposed for recognition and localization of categories in cluttered images. Modifications to allow for a correct deployment for 3D data are discussed. Moreover, we propose modifications to three design points within the structure of a standard ISM to enhance its effectiveness for the categorization of databases entries, either 3D or 2D: namely, codebook size and composition, codeword activation strategy and vote weight strategy. Experimental results on two standard 3D datasets allow us to discuss the positive impact of the proposed modifications as well as to show the performance in recognition accuracy yielded by our approach compared to the state of the art.

Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for a... more Recursive Bayesian Estimation (RBE) is a widespread solution for visual tracking as well as for applications in other domains requiring hidden state estimation. Although theoretically sound and unquestionably powerful, from a practical point of view RBE suffers from the assumption of complete a priori knowledge of the transition model, that is typically unknown. The use of wrong a priori transition model may lead to large estimation errors or even to divergence.
This work proposes to prevent these problems, in case of fully observable systems, learning the transition model on-line via Support Vector Regression. An application of this general framework is proposed in the context of linear/Gaussian systems and shown to be superior to a standard, non adaptive solution.

This paper deals with local 3D descriptors for surface matching. First, we categorize existing me... more This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms.
Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and
robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.